
'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     UpperCamelCase_        =							"""pt"""
elif is_tf_available():
     UpperCamelCase_        =							"""tf"""
else:
     UpperCamelCase_        =							"""jax"""


class 	a_	(UpperCAmelCase__				,      unittest.TestCase     ):
       __lowerCAmelCase     :					Union[str, Any]     =   PerceiverTokenizer
       __lowerCAmelCase     :					List[Any]     =   False




       def 				__UpperCamelCase							(  self	):
         super().setUp()
         _lowerCAmelCase							:	Any 					=       PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname	)




       @cached_property
       def 				__UpperCamelCase							(  self	):
         return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""	)




       def 				__UpperCamelCase							(  self , **snake_case_	):
         return self.tokenizer_class.from_pretrained(self.tmpdirname , **_a	)




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_=False , snake_case_=2_0 , snake_case_=5	):
         _lowerCAmelCase							:	Union[str, Any] 					=       []
         for i in range(len(_a	)	):
           try:
             _lowerCAmelCase							:	Optional[Any] 					=       tokenizer.decode([i] , clean_up_tokenization_spaces=_a	)
           except UnicodeDecodeError:
             pass
           toks.append((i, tok)	)

         _lowerCAmelCase							:	Any 					=       list(filter(lambda snake_case_	: re.match(r"""^[ a-zA-Z]+$""" , t[1]	) , _a	)	)
         _lowerCAmelCase							:	List[Any] 					=       list(filter(lambda snake_case_	: [t[0]] == tokenizer.encode(t[1] , add_special_tokens=_a	) , _a	)	)
         if max_length is not None and len(_a	) > max_length:
           _lowerCAmelCase							:	Tuple 					=       toks[:max_length]
         if min_length is not None and len(_a	) < min_length and len(_a	) > 0:
           while len(_a	) < min_length:
             _lowerCAmelCase							:	Optional[int] 					=       toks + toks
        # toks_str = [t[1] for t in toks]
         _lowerCAmelCase							:	Optional[Any] 					=       [t[0] for t in toks]

         # Ensure consistency
         _lowerCAmelCase							:	List[Any] 					=       tokenizer.decode(_a , clean_up_tokenization_spaces=_a	)
         if " " not in output_txt and len(_a	) > 1:
           _lowerCAmelCase							:	List[Any] 					=       (
               tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_a	)
               + """ """
               + tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_a	)
           )
         if with_prefix_space:
           _lowerCAmelCase							:	List[str] 					=       """ """ + output_txt
         _lowerCAmelCase							:	int 					=       tokenizer.encode(_a , add_special_tokens=_a	)
         return output_txt, output_ids




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Tuple 					=       self.perceiver_tokenizer
         _lowerCAmelCase							:	Optional[Any] 					=       """Unicode €."""
         _lowerCAmelCase							:	int 					=       tokenizer(_a	)
         _lowerCAmelCase							:	Optional[Any] 					=       [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded["""input_ids"""] , _a	)

         # decoding
         _lowerCAmelCase							:	Optional[int] 					=       tokenizer.decode(_a	)
         self.assertEqual(_a , """[CLS]Unicode €.[SEP]"""	)

         _lowerCAmelCase							:	Dict 					=       tokenizer("""e è é ê ë"""	)
         _lowerCAmelCase							:	List[str] 					=       [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded["""input_ids"""] , _a	)
         # decoding
         _lowerCAmelCase							:	Optional[int] 					=       tokenizer.decode(_a	)
         self.assertEqual(_a , """[CLS]e è é ê ë[SEP]"""	)

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""	)	) , """[CLS]e è é ê ë[SEP]"""	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Tuple 					=       self.perceiver_tokenizer
         _lowerCAmelCase							:	Optional[int] 					=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         # fmt: off
         _lowerCAmelCase							:	Optional[Any] 					=       [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         _lowerCAmelCase							:	Dict 					=       tokenizer(_a , padding=_a , return_tensors=_a	)
         self.assertIsInstance(_a , _a	)

         if FRAMEWORK != "jax":
           _lowerCAmelCase							:	int 					=       list(batch.input_ids.numpy()[0]	)
         else:
           _lowerCAmelCase							:	str 					=       list(batch.input_ids.tolist()[0]	)

         self.assertListEqual(_a , _a	)

         self.assertEqual((2, 3_8) , batch.input_ids.shape	)
         self.assertEqual((2, 3_8) , batch.attention_mask.shape	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	str 					=       self.perceiver_tokenizer
         _lowerCAmelCase							:	Tuple 					=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         _lowerCAmelCase							:	Dict 					=       tokenizer(_a , padding=_a , return_tensors=_a	)
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn("""input_ids""" , _a	)
         self.assertIn("""attention_mask""" , _a	)
         self.assertNotIn("""decoder_input_ids""" , _a	)
         self.assertNotIn("""decoder_attention_mask""" , _a	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	str 					=       self.perceiver_tokenizer
         _lowerCAmelCase							:	int 					=       [
             """Summary of the text.""",
             """Another summary.""",
         ]
         _lowerCAmelCase							:	Dict 					=       tokenizer(
             text_target=_a , max_length=3_2 , padding="""max_length""" , truncation=_a , return_tensors=_a	)
         self.assertEqual(3_2 , targets["""input_ids"""].shape[1]	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Any 					=       self.get_tokenizers()
         for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'	):
             self.assertNotEqual(tokenizer.model_max_length , 4_2	)

        # Now let's start the test
         _lowerCAmelCase							:	str 					=       self.get_tokenizers()
         for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'	):
             # Isolate this from the other tests because we save additional tokens/etc
             _lowerCAmelCase							:	Dict 					=       tempfile.mkdtemp()

             _lowerCAmelCase							:	Any 					=       """ He is very happy, UNwant\u00E9d,running"""
             _lowerCAmelCase							:	Optional[int] 					=       tokenizer.encode(_a , add_special_tokens=_a	)
             tokenizer.save_pretrained(_a	)

             _lowerCAmelCase							:	Any 					=       tokenizer.__class__.from_pretrained(_a	)
             _lowerCAmelCase							:	Tuple 					=       after_tokenizer.encode(_a , add_special_tokens=_a	)
             self.assertListEqual(_a , _a	)

             shutil.rmtree(_a	)

         _lowerCAmelCase							:	Tuple 					=       self.get_tokenizers(model_max_length=4_2	)
         for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'	):
             # Isolate this from the other tests because we save additional tokens/etc
             _lowerCAmelCase							:	Optional[Any] 					=       tempfile.mkdtemp()

             _lowerCAmelCase							:	Union[str, Any] 					=       """ He is very happy, UNwant\u00E9d,running"""
             tokenizer.add_tokens(["""bim""", """bambam"""]	)
             _lowerCAmelCase							:	int 					=       tokenizer.additional_special_tokens
             additional_special_tokens.append("""new_additional_special_token"""	)
             tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}	)
             _lowerCAmelCase							:	Any 					=       tokenizer.encode(_a , add_special_tokens=_a	)
             tokenizer.save_pretrained(_a	)

             _lowerCAmelCase							:	List[Any] 					=       tokenizer.__class__.from_pretrained(_a	)
             _lowerCAmelCase							:	Tuple 					=       after_tokenizer.encode(_a , add_special_tokens=_a	)
             self.assertListEqual(_a , _a	)
             self.assertIn("""new_additional_special_token""" , after_tokenizer.additional_special_tokens	)
             self.assertEqual(after_tokenizer.model_max_length , 4_2	)

             _lowerCAmelCase							:	int 					=       tokenizer.__class__.from_pretrained(_a , model_max_length=4_3	)
             self.assertEqual(tokenizer.model_max_length , 4_3	)

             shutil.rmtree(_a	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Tuple 					=       []
         if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

         if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

         for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer_utils.save_pretrained(_a	)

             with open(os.path.join(_a , """special_tokens_map.json"""	) , encoding="""utf-8"""	) as json_file:
               _lowerCAmelCase							:	str 					=       json.load(_a	)

             with open(os.path.join(_a , """tokenizer_config.json"""	) , encoding="""utf-8"""	) as json_file:
               _lowerCAmelCase							:	List[str] 					=       json.load(_a	)

             _lowerCAmelCase							:	str 					=       [f'<extra_id_{i}>' for i in range(1_2_5	)]

             _lowerCAmelCase							:	Optional[Any] 					=       added_tokens_extra_ids + [
                 """an_additional_special_token"""
             ]
             _lowerCAmelCase							:	Union[str, Any] 					=       added_tokens_extra_ids + [
                 """an_additional_special_token"""
             ]

             with open(os.path.join(_a , """special_tokens_map.json"""	) , """w""" , encoding="""utf-8"""	) as outfile:
               json.dump(_a , _a	)
             with open(os.path.join(_a , """tokenizer_config.json"""	) , """w""" , encoding="""utf-8"""	) as outfile:
               json.dump(_a , _a	)

             # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
             # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
             # "special_tokens_map.json" files
             _lowerCAmelCase							:	Any 					=       tokenizer_class.from_pretrained(
                 _a , )
             self.assertIn(
                 """an_additional_special_token""" , tokenizer_without_change_in_init.additional_special_tokens	)
             self.assertEqual(
                 ["""an_additional_special_token"""] , tokenizer_without_change_in_init.convert_ids_to_tokens(
                     tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]	)	) , )

             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             _lowerCAmelCase							:	Any 					=       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" , lstrip=_a	)]
             _lowerCAmelCase							:	Optional[Any] 					=       tokenizer_class.from_pretrained(
                 _a , additional_special_tokens=_a , )

             self.assertIn("""a_new_additional_special_token""" , tokenizer.additional_special_tokens	)
             self.assertEqual(
                 ["""a_new_additional_special_token"""] , tokenizer.convert_ids_to_tokens(
                     tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]	)	) , )




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Optional[int] 					=       self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]	) , """�"""	)




       def 				__UpperCamelCase							(  self	):
         pass




       def 				__UpperCamelCase							(  self	):
         pass




       def 				__UpperCamelCase							(  self	):
         pass




       def 				__UpperCamelCase							(  self	):
         pass







       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Optional[Any] 					=       self.get_tokenizers(fast=_a , do_lower_case=_a	)
         for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'	):
             _lowerCAmelCase							:	List[str] 					=       ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
             _lowerCAmelCase							:	str 					=       tokenizer.convert_tokens_to_string(_a	)

             self.assertIsInstance(_a , _a	)








"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				"""▁"""

lowerCAmelCase				:     str					=				{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase				:     Union[str, Any]					=				{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase				:     Any					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase				:     List[Any]					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}



def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   collections.OrderedDict()
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as reader:
						lowerCamelCase      =   reader.readlines()
			for index, token in enumerate(snake_case__							):
						lowerCamelCase      =   token.rstrip("""\n"""							)
						lowerCamelCase      =   index
			return vocab






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[UNK]"						,					_a="[PAD]"						,					_a="[CLS]"						,					_a="[MASK]"						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_a						,					eos_token=_a						,					sep_token=_a						,					unk_token=_a						,					pad_token=_a						,					cls_token=_a						,					mask_token=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)

							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							lowerCamelCase      =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCamelCase      =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10					):
										lowerCamelCase      =   f'[unused{i}]'
										lowerCamelCase      =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCamelCase      =   12
							lowerCamelCase      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_a					)





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d
							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_a						,					token_ids_a=_a						,					already_has_special_tokens=_a					)

							if token_ids_a is None:
										return ([0] * len(_a					)) + [1]
							return ([0] * len(_a					)) + [1] + ([0] * len(_a					)) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							lowerCamelCase      =   [self.sep_token_id]

							if token_ids_a is None:
										return len(token_ids_a + sep					) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.fairseq_offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase      =   self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   """""".join(_a					).replace(_a						,					""" """					).strip()
							return out_string





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""

							if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
							lowerCamelCase      =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep



